Papers by Quentin Anthony
RWKV: Reinventing RNNs for the Transformer Era (2023.findings-emnlp)
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Bo Peng, Eric Alcaide, Quentin Anthony, Alon Albalak, Samuel Arcadinho, Stella Biderman, Huanqi Cao, Xin Cheng, Michael Chung, Leon Derczynski, Xingjian Du, Matteo Grella, Kranthi Gv, Xuzheng He, Haowen Hou, Przemyslaw Kazienko, Jan Kocon, Jiaming Kong, Bartłomiej Koptyra, Hayden Lau, Jiaju Lin, Krishna Sri Ipsit Mantri, Ferdinand Mom, Atsushi Saito, Guangyu Song, Xiangru Tang, Johan Wind, Stanisław Woźniak, Zhenyuan Zhang, Qinghua Zhou, Jian Zhu, Rui-Jie Zhu
| Challenge: | recurrent neural networks struggle to match the performance of Transformers due to limitations in parallelization and scalability. |
| Approach: | They propose a model architecture that combines the efficient parallelizable training of transformers with the efficient inference of RNNs. |
| Outcome: | The proposed model performs on par with similarly sized RNNs, suggesting future work can leverage this architecture to create more efficient models. |
Transformers: State-of-the-Art Natural Language Processing (2020.emnlp-demos)
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Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, Alexander Rush
| Challenge: | Transformers is an open-source library that aims to open up advances in natural language processing to the wider machine learning community. |
| Approach: | they propose an open-source library that aims to open up advances in machine learning to the wider community. |
| Outcome: | Transformers is an open-source library with the goal of opening up these advances to the wider machine learning community. |
trlX: A Framework for Large Scale Reinforcement Learning from Human Feedback (2023.emnlp-main)
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Alexander Havrilla, Maksym Zhuravinskyi, Duy Phung, Aman Tiwari, Jonathan Tow, Stella Biderman, Quentin Anthony, Louis Castricato
| Challenge: | Current RLHF paradigms rely on Proximal Policy Optimization (PPO), which quickly becomes a challenge to implement and scale up to large architectures. |
| Approach: | They propose an open-source framework for reinforcement learning from human feedback . it allows for offline fine-tuning of large language models . |
| Outcome: | The framework can be used to fine-tune models up to and exceeding 70 billion parameters. |